A Simplified Framework for the Detection of Intracranial Hemorrhage in CT Brain Images Using Deep Learning

Kumaravel, P.; Mohan, Sasikala; Arivudaiyanambi, Janani; Shajil, Nijisha; Venkatakrishnan, Hari Nishanthi

doi:10.2174/1573405617666210218100641

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…The discrepancy rate between primary RR and AI analysis was 3.3%. Working with AI holds opportunities and challenges for both radiologist and skillful clinicians who, in addition to maintaining their expertise also need to be aware of the pitfalls of the specific software solutions [ 43 , 44 ]. Specifically, both radiologists and clinicians can and should consult a patient’s past and current medical history, previous imaging data, and additional CT reconstructions to form a comprehensive diagnosis where results appear inconclusive ( Fig 5 and S5 Fig ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

et al. 2021

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Background Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services. Methods In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors. Results 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results. Conclusion Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT. Trial registration German Clinical Trials Register (DRKS-ID: DRKS00023593).

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Section: Discussionmentioning

confidence: 99%

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

et al. 2021

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“…The data sources analysis was divided into single [ 20 , 22 , 24 , 26 , 27 , 30 , 32 – 34 , 36 – 39 , 41 , 42 , 47 , 48 , 50 ] or multiple [ 21 , 23 – 25 , 27 , 28 , 31 , 34 , 35 , 43 , 45 – 49 ]. These results were not significant for the sensitivity (p-value = 0.6879), specificity (p-value = 0.6494), and DOR (p-value = 0.7272) ( Additional file 1 : Figures S9–S11).…”

Section: Resultsmentioning

confidence: 99%

“…Detection of ICH by ML in systematic studies may decrease the time to diagnosis, which is crucial for clinical because approximately most of ICH in accordance with death occurs within the primary hours [ 53 ]. This meta-analysis demonstrated that ResNet algorithms could detect ICHs accurately with retrospective and non-randomized data [ 22 , 31 , 33 , 37 , 38 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the high AUC of the included trials could not correctly represent the performance of the algorithm's therapeutic benefit [ 54 ]. Initially, the range of AUC among studies was 0.608 to 1 that Neural Networks (NNs) learning such as CNN, ResNet, and RNN had a higher rate from other ML algorithms [ 20 , 21 , 23 , 24 , 26 – 29 , 31 , 33 , 37 – 39 , 43 , 44 , 46 , 49 ]. In other words, this result suggested that NNs algorithms in the big data can improve the rate of AUC which it is a useful way to detect a good model and positive and negative target classes.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

Maghami,

Sattari,

Tahmasbi

et al. 2023

BioMed Eng OnLine

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Background This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. Methods Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used. Results At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88–0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918–0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78–459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854–0.973, I2 = 93%). Conclusion This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).

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“…In the study performed by Kumaravel et al , using the CQ500 open data set, the hemorrhage types were evaluated in general, and the presence or absence of hemorrhages was examined. [ 45 ] AlexNet, an old deep learning model, was used and a success rate of around 99% was achieved. [ 45 ] This shows that the images in the dataset are not very complex in terms of classification.…”

Section: Discussionmentioning

confidence: 99%

A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model

Kaya

Gençtürk

Gülağız

2023

Ulus Travma Acil Cerrahi Derg

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BACKGROUND: The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way to quickly warn the responsible doctor. METHODS: A two-level AI-based hybrid method was developed. The proposed model uses the mask-region convolutional neural network (Mask R-CNN) technique, which is a deep learning model, in the hemorrhagic region’s mask generation stage, and a problem-specific, optimized support vector machines (SVM) technique which is a machine learning model in the binary classification stage. Furthermore, the bee colony algorithm was used for the optimization of SVM algorithms’ parameters. RESULTS: In the first stage, the mean average precision (mAP) value was obtained as 0.754 when the intercept over union (IOU) value was taken as 0.5 with the Mask R-CNN architecture used. At the same time, when a 5-fold cross-validation was applied, the mAP value was obtained 0.736. With the hyperparameter optimization for both Mask R-CNN and the SVM algorithm, the accuracy of the two-level classification process was obtained as 96.36%. Furthermore, final false-negative rate and false-positive rate values were obtained as 6.20%, and 2.57%, respectively. CONCLUSION: With the proposed model, both the detection of hemorrhage and the presentation of the suspicious area to the physician were performed more successfully on two dimensional (2D) images with low cost and high accuracy compared to similar studies and today’s interpretations with telemedicine techniques.

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A Simplified Framework for the Detection of Intracranial Hemorrhage in CT Brain Images Using Deep Learning

Cited by 19 publications

References 15 publications

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model

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